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Zheng Duan

Universitetslektor

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Integration of remote sensing and Mexican water quality monitoring system using an extreme learning machine

Författare

  • Leonardo F. Arias-Rodriguez
  • Zheng Duan
  • José de Jesús Díaz-Torres
  • Mónica Basilio Hazas
  • Jingshui Huang
  • Bapitha Udhaya Kumar
  • Ye Tuo
  • Markus Disse

Summary, in English

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 =0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2021-06-02

Språk

Engelska

Publikation/Tidskrift/Serie

Sensors

Volym

21

Issue

12

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Remote Sensing
  • Oceanography, Hydrology, Water Resources

Nyckelord

  • Chlorophyll-a
  • Extreme learning machine
  • Inland waters
  • Landsat 8 OLI
  • Secchi disk depth
  • Sentinel 2 MSI
  • Sentinel 3 OLCI
  • Support vector regression
  • Turbidity
  • Water quality monitoring system

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 1424-8220